BTL Mark: Resolve interoperability issues & increase buyer confidence
Lifetime Learning for Smart Things Everywhere
Thousands of AI systems in each building will require tools to manage the rapid evolution algorithms.
For decades, from even before we called
everything the IoT (Internet of Things), maintenance has been the
barrier to digital sensing and operating of the physical world. Wired
sensors were reliable but expensive to install, and often an esthetic
nightmare once installed. With self-power, sensors became cheap enough
to put everywhere, but faced a new challenge—maintenance.
For a long time, deployments were
limited by battery life. Many initiatives were short-lived, running
until the batteries wore out. Changing the batteries was expensive,
sometimes more than the initial installation. Committed organizations
developed scheduled battery changes to control costs, just as they had
done before for re-lamping projects.
We solved that problem by making sensors
so cheap we could just leave them and install replacements. Or we
(notably members of the EnOcean Alliance) tuned communications to be so
light-weight that in situ energy harvesting could keep systems working.
Now we face yet another maintenance challenge, that of intelligence management.
Today’s sensors have become smarter,
sometimes referred to by the indeterminate name “edge devices.” Sensors
and Edge Devices likely transmitted more than 20 zettabytes of data for
central storage last year, although there are no firm estimates on 2017
data gathering. With that much data being stored, the communications
requirement was easily in yottabytes.
This much data creates a new challenge.
The IoT not only requires that we get actionable information that
matters, but that we get it before it is too late to matter. There is
too much data and too many situations to rely on timely central
drinking this firehose of data, we are starting to rely on sips at the
edge. Edge Devices are making the initial decisions as to what data
means, and what data needs to be brought into the middle. Local
decisions are made faster and more accurately locally, without
interference from temporary higher priorities in the cloud. For all but
the simplest scenarios, this model requires learning at the edges.
There are large now open source libraries now of Artificial
Intelligence (AI) code for Raspberry Pi and Arduino.
This presents a new maintenance problem, managing and updating AI routines and algorithms.
The big software companies are preparing the tools we will need. Thousands of AI systems in each building will require tools to manage the rapid evolution algorithms. New algorithms will require managed roll-outs Rapid evolution forces diversity of algorithm and information as systems will change far faster than their installed life. Oracle is pushing GraphPipe, an open source software project for efficiently deploying and managing AI models at scale. Microsoft is right there with them, with large platform management announcements expected this Fall.
The problem of managing intelligence in millions of devices is solved already before most people know they have the problem.
In the last year, Pi architecture
devices have blown right past the $40 and even $20 price points, with
full systems expected for $7 and perhaps $4. Arduino platforms not only
run open source Linux and Android but with open source hardware offer
potential easy integration directly onto integrated specialty hardware
The barriers to fully intelligent small
systems across every aspect of buildings are falling even faster than
pioneers such as Alper Üzmezler and the Project Sandstar for smart
controls have publicly projected. It is my personal belief that while
full platforms such as the Pi have higher initial costs, in part
because they include a GPU not needed to manage a display, that this
means they are pre-adapted for high-speed signal processing. This will
not play out slowly.
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